Data-driven model for control and optimization of hydrocarbon production

ABSTRACT

A system for controlling and optimizing hydrocarbon production may include sensors that capture sensor data pertaining to wellhead pressure values in a well. The system may also include a multiphase flow meter that captures production data pertaining to multiphase production flow rates of the well. The system may include an access module to access an estimated parameter value associated with a second time and a parameter that pertains to production. The estimated parameter value is predicted by a data-driven model for describing production fluid dynamics of the well, based on the sensor data and the production data obtained at a first time. The system includes a processor to update the data-driven model using a data assimilation algorithm and the production data received at the second time. The processor generates, using the updated data-driven model, an optimal control setting of a control tool for causing an adjustment to a production system.

BACKGROUND

In the petroleum industry, the production of hydrocarbons is a complexprocess that is governed by complex dynamics of a coupledwellbore-reservoir system. The uncontrolled operation of a well does notguarantee maximized production. Moreover, the uncontrolled operation ofa well may lead to serious danger to the health and life of the peopleworking on the well, to the environment, and to the equipment of thewell.

Accordingly, there is a need for an intelligent production controlsystem for improving the production of hydrocarbon fluid from oil andgas wells.

SUMMARY

This summary is provided to introduce concepts that are furtherdescribed below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

In general, in one aspect, embodiments disclosed herein relate to asystem for controlling and optimizing hydrocarbon production. The systemincludes one or more sensors arranged to capture sensor data pertainingto one or more wellhead pressure values in a well. The system includes amultiphase flow meter arranged to capture production data pertaining tomultiphase production flow rates of the well. The system includes anaccess module configured to access an estimated parameter valueassociated with a second time and a parameter. The parameter pertains toproduction from the well. The estimated parameter value is predicted bya data-driven model for describing production fluid dynamics of thewell, based on the sensor data and the production data obtained at afirst time. The system includes one or more hardware processorsconfigured to update the data-driven model using a data assimilationalgorithm and the production data received during a production processat the second time. The one or more hardware processors are furtherconfigured to generate, using the updated data-driven model, an optimalcontrol setting of a control tool for causing an adjustment to aproduction system.

In general, in one aspect, embodiments disclosed herein relate to amethod for controlling and optimizing hydrocarbon production. The methodincludes accessing an estimated parameter value associated with a secondtime and a parameter. The parameter pertains to production from a well.The estimated parameter value is predicted by a data-driven model fordescribing production fluid dynamics of the well, based on sensor dataand production data obtained at a first time. The method includesupdating the data-driven model using a data assimilation algorithm andthe production data obtained during a production process at the secondtime. The updating is performed using one or more hardware processors.The method includes generating, using the updated data-driven model, anoptimal control setting of a control tool for causing an adjustment to aproduction system.

In general, in one aspect, embodiments disclosed herein relate to anon-transitory machine-readable storage medium. The non-transitorymachine-readable storage medium includes instructions that, whenexecuted by one or more processors of a machine, cause the machine toperform operations. The operations include accessing an estimatedparameter value associated with a second time and a parameter. Theparameter pertains to production from a well. The estimated parametervalue is predicted by a data-driven model for describing productionfluid dynamics of the well, based on sensor data and production dataobtained at a first time. The operations include updating thedata-driven model using a data assimilation algorithm and the productiondata obtained during a production process at the second time. Theoperations include generating, using the updated data-driven model, anoptimal control setting of a control tool for causing an adjustment to aproduction system.

Other aspects and advantages of the claimed subject matter will beapparent from the following description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings.

FIG. 1 illustrates a system, according to one or more exampleembodiments.

FIG. 2 is a block diagram that illustrates a production control system,according to one or more example embodiments.

FIG. 3 is a flow diagram that illustrates an algorithm for dataassimilation using the production control system, according to one ormore example embodiments.

FIG. 4 illustrates a graphical representation of optimal controlsettings, according to one or more example embodiments.

FIG. 5 is a flowchart illustrating operations of the production controlsystem in performing a method for controlling and optimizing hydrocarbonproduction, according to one or more example embodiments.

FIGS. 6A and 6B illustrate a computing system, according to one or moreexample embodiments.

DETAILED DESCRIPTION

Example systems and methods for controlling and optimizing hydrocarbonproduction using a data-driven model are described. Unless explicitlystated otherwise, components and functions are optional and may becombined or subdivided. Similarly, operations may be combined orsubdivided, and their sequence may vary.

In the following detailed description of embodiments of the disclosure,numerous specific details are set forth in order to provide a morethorough understanding of the disclosure. However, it will be apparentto one of ordinary skill in the art that the disclosure may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail to avoid unnecessarily complicatingthe description.

Throughout the application, ordinal numbers (e.g., first, second, orthird) may be used as an adjective for an element (that is, any noun inthe application). The use of ordinal numbers is not to imply or createany particular ordering of the elements nor to limit any element tobeing only a single element unless expressly disclosed, such as usingthe terms “before,” “after,” “single,” and other such terminology.Rather, the use of ordinal numbers is to distinguish between theelements. By way of an example, a first element is distinct from asecond element, and the first element may encompass more than oneelement and succeed (or precede) the second element in an ordering ofelements.

According to some example embodiments, a production control system maybe used to optimize the hydrocarbon production from a well using anon-linear, data-driven Artificial Intelligence (AI) model. An analysisof the relationship between the dynamics of flow variables (e.g.,multiphase flow rates at the wellhead, wellbore pressures, andtemperatures) associated with the hydrocarbon production from a well andof wellhead pressure values at various times may facilitate a moreaccurate prediction of future multiphase flow rates form the well. Suchanalysis may be represented in the data-driven model that may be used,by the production control system, to generate optimal control settingsfor a choke valve for improved control of the production from the well.The data-driven model may be defined based on available production dataand sensor data using system identification techniques, such as adynamic mode decomposition (DMD) algorithm. In addition, the data-drivenmodel may be corrected using data assimilation methods.

The production control system provides improvements over existingmethods by incorporating the data-driven model generated (hereinafteralso “trained” or “established”) based on a DMD algorithm that usesmeasurements taken by wellbore instrumentation, such as sensors, gauges,and three-phase separators. The data-driven model excludes unobservable(unmeasured) components, such as reservoir-based variables, fromconsideration. The production control system, using the DMD algorithm,extracts dynamically-relevant process features from time-resolvedexperimental data associated with a well. Then, the production controlsystem generates a low-dimensional, data-driven model for predictingfuture multiphase flow rates for the well, based on thedynamically-relevant process features. Unlike the existing conventionalmodels, the low-dimensional, data-driven model predicts optimal controlsettings (e.g., rate and pressure at which production fluids progressthrough a pipeline) for the control of production from the well moreaccurately and over a longer forecast horizon. In addition, the use of alow-dimensional model improves the computation speed of the productioncontrol system as compared to existing control systems.

In some example embodiments, the data-driven model is generated based onsensor data obtained using downhole temperature and pressure sensors (orgauges) and based on available production data pertaining to multiphaseflow rates (i.e., oil, gas, and water) determined using a multiphaseflow meter or test separator. The sensor data and the production datamay be matched based on time stamps associated with the sensor data andthe production data. The production control system initializes thedata-driven model with the dynamically relevant process featuresextracted from the sensor data and the production data.

In some example embodiments, the data-driven model explains therelationship between the control of the well and the dynamics of flowvariables in the following pseudo—-pseudo-linear way:

X_(t + 1) = A_(DMD) ⋅ X_(t) + B_(DMD) ⋅ u_(t),

where X is a state vector, which includes multiphase rates at thewellhead, wellbore pressures, and temperatures, t refers to a time stepindex, and u is a control vector, which includes values of wellheadpressure adjustable through the topside choke. A and B are the matricesdefining the system dynamics, which are extracted from the data usingthe DMD algorithm.

Upon extracting the dynamically relevant process data from the sensordata and the production data, the production control system trains thedata-driven model, based on the extracted dynamically relevant processdata, to predict the estimated parameter value associated with a futuretime. The training process can be formulated in the following paradigm.All the dynamic information is considered as time series x_(i)(t) andu_(i)(t), where x is the vector of the target parameters (e.g., the flowrates of oil, gas, and water) and u represents the control vector ofwellhead pressures. Mathematical formulation of the prediction processcan be given as follows: it is necessary to estimate the output value ofx_(i) at the time t given a time series of input features with temporallength of l, which in case the measurements are equally spaced in timecorresponds to the shifted time window of [t - l, t]. Given the set oftraining data, where the target flow rates are known, the training datais split into a finite number of overlapping sequences of length lshifted by one time step from each other. The resulting training inputmatrix can be represented as follows:

$X = \begin{bmatrix}x_{0} & \cdots & & x_{TR - l} \\x_{1} & \cdots & & x_{TR - l + 1} \\ \vdots & \ddots & & \vdots \\x_{l} & \cdots & & x_{TR}\end{bmatrix}.$

Similarly, the array of control inputs u is defined. Here, the subscriptTR corresponds to the number of data points in the training sequence.The data-driven model defines a numerical operator, which maps everycolumn of X(t) and u(t) to the entries of vector X(t+1).

X(t), u(t) → X(t + 1).

In one or more embodiments, this mapping is defined by DMD, i.e., a DMDoperator (also hereinafter “DMD model”) is introduced which relates X(t)and u(t) to X(t+1). Once the DMD operator is trained, the followingequation can be used to estimate the values of multiphase flow rates,wellbore pressures, and temperatures at time t+1:

X_(t + 1) = A_(DMD) ⋅ X_(t) + B_(DMD) ⋅ u_(t)

Since the measured data is used to prepare a data-driven model accordingto X(t), u(t) → X(t + 1), then any measurement uncertainty wouldpropagate to matrices A_(DMD) and B_(DMD) defining the DMD operator andeventually would cause a deviation of the predicted variables from themeasured values. The typical sources of uncertainty are related to thequality of the sensor used, whether the information from the sensors iscontinuously supplied, or there are any gaps in the received data.

The data-driven model can be used to predict multiphase flow rates, suchas the flow rates of oil, gas, and water, from a well when theassociated uncertainty is minimal. For highly non-linear, time-varyingdynamics, the data-driven model may eventually diverge from actualmeasured production values due to accumulated changes in systemconditions. To mitigate this, data assimilation techniques based on theKalman Filter may be used. Such data assimilation techniques enableonline correction of the structure of the data-driven model byintegrating newly acquired measurements into the data-driven model. Forexample, the components of matrices A and B for model predictions may beadjusted to match actual production measurement values.

Once the data-driven model is generated, the data-driven model may beused to maximize the production of the well by solving an optimizationproblem to compute a new control input vector u to be applied to thedata-driven model. The result of the application of the new controlvector u is the new vector of state variables X. The control inputvector may be used, by the production control system, to control theamount of opening of a choke valve (e.g., a topside choke valve)associated with the well. The values in the control vector correspond tothe choke opening. Hence, by computing a new value of a control vector,the choke opening can be defined and set. The production control systemmay control the production of the well by adjusting the position of thechoke valve to allow the flow of an optimal level of hydrocarbonproduction.

FIG. 1 shows a schematic diagram of a system, in accordance with one ormore embodiments. FIG. 1 illustrates a well environment 100 thatincludes a hydrocarbon reservoir (“reservoir”) 102 located in asubsurface hydrocarbon-bearing formation (“formation”) 104 and a wellsystem 106. The hydrocarbon-bearing formation 104 may include a porousor fractured rock formation that resides underground, beneath theearth’s surface (“surface”) 108. In the case of the well system 106being a hydrocarbon well, the reservoir 102 may include a portion of thehydrocarbon-bearing formation 104. The hydrocarbon-bearing formation 104and the reservoir 102 may include different layers of rock havingvarying characteristics, such as varying degrees of permeability,porosity, capillary pressure, and resistivity. In the case of the wellsystem 106 being operated as a production well, the well system 106 mayfacilitate the extraction (or “production”) of hydrocarbons from thereservoir 102.

In some embodiments disclosed herein, the well system 106 includes a rig101, a wellbore 120, a well sub-surface system 122, a well surfacesystem 124, and an operation system 126. The operation system 126 maycontrol various operations of the well system 106, such as wellproduction operations, well completion operations, well maintenanceoperations, and reservoir monitoring, assessment and developmentoperations. In some embodiments, the operation system 126 includes acomputer system that is the same as or similar to computing system 500or 514 described below in FIGS. 5A and 5B, and the accompanyingdescriptions.

The rig 101 is the machine used to drill a borehole to form the wellbore120. Major components of the rig 101 include the mud tanks, the mudpumps, the derrick or mast, the drawworks, the rotary table or topdrive,the drillstring, the power generation equipment, and auxiliaryequipment.

The wellbore 120 includes a bored hole (i.e., borehole) that extendsfrom the surface 108 into a target zone of the hydrocarbon-bearingformation 104, such as the reservoir 102. An upper end of the wellbore120, terminating at or near the surface 108, may be referred to as the“up-hole” end of the wellbore 120, and a lower end of the wellbore,terminating in the hydrocarbon-bearing formation 104, may be referred toas the “downhole” end of the wellbore 120. The wellbore 120 mayfacilitate the circulation of drilling fluids during drillingoperations, the flow of hydrocarbon production (“production”) 121 (e.g.,oil, gas, or both) from the reservoir 102 to the surface 108 duringproduction operations, the injection of substances (e.g., water) intothe hydrocarbon-bearing formation 104 or the reservoir 102 duringinjection operations, or the communication of monitoring devices (e.g.,logging tools) into the hydrocarbon-bearing formation 104 or thereservoir 102 during monitoring operations (e.g., during in situ loggingoperations).

In some embodiments, during operation of the well system 106, theoperation system 126 collects and records wellhead data 140 for the wellsystem 106. The wellhead data 140 may include, for example, a record ofmeasurements of wellhead pressure values (P_(wh)) (e.g., includingflowing wellhead pressure values), wellhead temperature values (T_(wh))(e.g., including flowing wellhead temperature values), wellheadmultiphase production rates (Q_(wh)) over some or all of the life of thewell (106), and water cut data. In some embodiments, the measurementvalues are recorded in real-time, and are available for review or usewithin seconds, minutes, or hours of the condition being sensed (e.g.,the measurements are available within 1 hour of the condition beingsensed). In such an embodiment, the wellhead data 140 may be referred toas “real-time” wellhead data 140. Real-time wellhead data 140 may enablean operator of the well 106 to assess a relatively current state of thewell system 106, and make real-time decisions regarding development ormanagement of the well system 106 and the reservoir 102, such ason-demand adjustments in regulation of production flow from the well.

In some embodiments, the well sub-surface system 122 includes casinginstalled in the wellbore 120. For example, the wellbore 120 may have acased portion and an uncased (or “open-hole”) portion. The cased portionmay include a portion of the wellbore having casing (e.g., casing pipeand casing cement) disposed therein. The uncased portion may include aportion of the wellbore not having casing disposed therein. In someembodiments, the casing includes an annular casing that lines the wallof the wellbore 120 to define a central passage that provides a conduitfor the transport of tools and substances through the wellbore 120. Forexample, the central passage may provide a conduit for lowering loggingtools into the wellbore 120, a conduit for the flow of production 121(e.g., oil and gas) from the reservoir 102 to the surface 108, or aconduit for the flow of injection substances (e.g., water) from thesurface 108 into the hydrocarbon-bearing formation 104. In someembodiments, the well sub-surface system 122 includes production tubinginstalled in the wellbore 120. The production tubing may provide aconduit for the transport of tools and substances through the wellbore120. The production tubing may, for example, be disposed inside casing.In such an embodiment, the production tubing may provide a conduit forsome or all of the production 121 (e.g., oil and gas) passing throughthe wellbore 120 and the casing.

In some embodiments, the well surface system 124 includes a wellhead130. The wellhead 130 may include a rigid structure installed at the“up-hole” end of the wellbore 120, at or near where the wellbore 120terminates at the Earth’s surface 108. The wellhead 130 may includestructures for supporting (or “hanging”) casing and production tubingextending into the wellbore 120. Production 121 may flow through thewellhead 130, after exiting the wellbore 120 and the well sub-surfacesystem 122, including, for example, the casing and the productiontubing. In some embodiments, the well surface system 124 includes flowregulating devices that are operable to control the flow of substancesinto and out of the wellbore 120. For example, the well surface system124 may include one or more production valves 132 that are operable tocontrol the flow of production 134. A production valve 132 may be fullyopened to enable unrestricted flow of production 121 from the wellbore120. Further, the production valve 132 may be partially opened topartially restrict (or “throttle”) the flow of production 121 from thewellbore 120. In addition, the production valve 132 may be fully closedto fully restrict (or “block”) the flow of production 121 from thewellbore 120, and through the well surface system 124.

In some embodiments, the wellhead 130 includes a choke assembly. Forexample, the choke assembly may include hardware with functionality foropening and closing the fluid flow through pipes in the well system 106.Likewise, the choke assembly may include a pipe manifold that may lowerthe pressure of fluid traversing the wellhead. As such, the chokeassembly may include a set of high-pressure valves and at least twochokes. These chokes may be fixed or adjustable or a mix of both.Redundancy may be provided so that if one choke is taken out of service,the flow can be directed through another choke. In some embodiments,pressure valves and chokes are communicatively coupled to the operationsystem 126. Accordingly, the operation system 126 may obtain wellheaddata regarding the choke assembly as well as transmit one or morecommands to components within the choke assembly in order to adjust oneor more choke assembly parameters.

Keeping with FIG. 1 , in some embodiments, the well surface system 124includes a surface sensing system 134. The surface sensing system 134may include sensors for sensing characteristics of substances, includingproduction 121, passing through or otherwise located in the well surfacesystem 124. The characteristics may include, for example, pressure,temperature and flow rate of production 121 flowing through the wellhead130, or other conduits of the well surface system 124, after exiting thewellbore 120. The surface sensing system 134 may also include sensorsfor sensing characteristics of the rig 101, such as bit depth, holedepth, drilling mudflow, hook load, rotary speed, etc.

In some embodiments, the surface sensing system 134 includes a surfacepressure sensor 136 operable to sense the pressure of production 151flowing through the well surface system 124, after it exits the wellbore120. The surface pressure sensor 136 may include, for example, awellhead pressure sensor that senses a pressure of production 121flowing through or otherwise located in the wellhead 130. In someembodiments, the surface sensing system 134 includes a surfacetemperature sensor 138 operable to sense the temperature of production151 flowing through the well surface system 124, after it exits thewellbore 120. The surface temperature sensor 138 may include, forexample, a wellhead temperature sensor that senses a temperature ofproduction 121 flowing through or otherwise located in the wellhead 130,referred to as “wellhead temperature” (T_(wh)). In some embodiments, thesurface sensing system 134 includes a flow rate sensor 139 operable tosense the flow rate of production 151 flowing through the well surfacesystem 124, after it exits the wellbore 120. The flow rate sensor 139may include hardware that senses a flow rate of production 121 (Q_(wh))passing through the wellhead 130. In some embodiments, downhole sensorsand gauges are operable to capture production-related data (e.g.,pressures, temperatures, etc.).

While FIG. 1 illustrates a configuration of components, otherconfigurations may be used without departing from the scope of thedisclosure. For example, various components in FIG. 1 may be combined tocreate a single component. As another example, the functionalityperformed by a single component may be performed by two or morecomponents.

FIG. 2 is a block diagram that illustrates a production control system214, and other parts of a system that interact with the productioncontrol system 214, according to one or more example embodiments. Theproduction control system 214 is shown as including one or more sensors216 (shown as the pressure sensor 136, the temperature sensor 138, andthe flow rate sensor 139 in FIG. 1 ), a multiphase flow meter 218, anaccess module 220, and one or more processors 222 (shown as theoperation system 126 in FIG. 1 ). The components of the productioncontrol system 214 are operatively connected and are configured tocommunicate with each other (e.g., via a bus, shared memory, a switch,wirelessly, etc.). In addition, the production control system 214 isconfigured to communicate with a data repository 202 and a control tool212.

The one or more sensors 216 are arranged to capture data associated witha parameter (e.g., a pressure or a temperature) over a certainproduction period. The multiphase flow meter 218 is arranged to capturedata pertaining to multiphase (e.g., gas, oil, and water components)production flow rates. The data captured by the one or more sensors 216may be stored as sensor data 204 in the data repository 202. The datacaptured by the multiphase flow meter 218 may be stored as productiondata 206 in the data repository 202. The access module 218 may accessthe sensor data 204 and the production data 206 and may use this data asinput for a system identification algorithm to generate a lower-orderdata-driven model that describes the multiphase flow production from awell. The lower-order model may be stored as data-driven model 208 inthe data repository 202.

The one or more processors 222 are configured, in some exampleembodiments, to extract dynamically-relevant process data from thesensor data 204 and the production data 206 using a dynamic modedecomposition (DMD) algorithm. The one or more processors 222 arefurther configured to train the data-driven model 208 based on theextracted dynamically-relevant process data.

In addition, the one or more processors 222 update the data-driven model208 using a data assimilation algorithm and production data receivedduring a production process. Further, a processor 222 generates, usingthe updated data-driven model 208, an optimal control setting of thecontrol tool 212 for causing an adjustment to a production system. Theprocessor 222 may generate an instruction 210 for the control tool 212to make an adjustment to the production system based on the optimalcontrol setting. The processor 222 may execute the instruction 210during the production process. The executing of the instruction causesthe control tool 212 to perform the adjustment to the production system.In some embodiments, the control tool 212 is a production valve. Incertain embodiments, the control tool 212 is a choke assembly. As shownin FIG. 2 , the control tool instructions 210 may be stored in the datarepository 202.

FIG. 3 is a flow diagram that illustrates an algorithm for dataassimilation using the production control system, according to one ormore example embodiments. According to certain example embodiments, oneor more steps shown in flow diagram 300 are performed by the productioncontrol system 214, which may also be executed on a computing system asshown in FIGS. 6A and 6B below.

In some example embodiments, at Step 302, sensor data 204 and productiondata 206 are utilized to initialize the data-driven model 208.Specifically, the production control system 214 calculates matricesA_(DMD) and B_(DMD) which define the data-driven model 208.

After the data-driven model 208 is initialized, the production controlsystem 214 (e.g., the processor 222) predicts, at Step 304, an estimatedvalue of a parameter. The estimated value is associated with aparticular future time. Depending on the formulation of the objectivefunction, the target criteria for optimization could be maximizing theoil recovery, minimizing the water cut, maximizing the net present value(NPV), etc.

Next, at Step 306, Kalman filter equations may be applied to thedata-driven model 208 to increase the accuracy of estimation ofparameter values. In some example embodiments, the production controlsystem 214 updates the data-driven model 208 with an actual measurementfor the parameter, that was obtained at the next time, t+1, and thecovariance matrix is minimized. The covariance matrix represents therelationship between a pair of different states and parameters. Byminimizing the covariance matrix, the uncertainty of a data-driven modelis reduced. The production control system 214 then transitions to a newstate, X(t + 1), after which the cycle starts over.

At each control step t, a Model Predictive Control (MPC) controllermeasures the current state of the system, X(t). The MPC controller is anadvanced mathematical method of system optimization, which is used tocontrol a process while satisfying a set of constraints. In thisexample, the control is performed by the control vector, and theconstraints are defined by a data-driven model. Then, to predict theparameter value for the particular time t+1, the production controlsystem 214 uses the data-driven model 208 to derive, at time t, an apriori state estimate for the next time step, t+1.

FIG. 4 illustrates a graphical representation of optimal controlsettings, according to one or more example embodiments. Upon updatingthe data-driven model 208 using the data assimilation algorithm andactual production data received during a production process, theproduction control system 214 generates, using the updated data-drivenmodel 208, an optimal control setting of a control tool for causing anadjustment to a production system. FIG. 4 shows the output thedata-driven model 208 as a series of constant choke settings in apredicted control input graph 406. The series is defined over a futurecontrol period (“prediction horizon”), and includes a number of sampletimes, t, t+1, t+2, ... , t+n.

In addition, FIG. 4 shows a required target graph 402 and a predictedtarget graph 404. The target graph 402 illustrates the expected resultsfor a target criterion, such as maximized oil recovery, minimized watercut, or maximized NPV. The predicted target graph 404 illustrates theresults of the MPC operation. In some instances, the predictions shouldconverge to expected values.

FIG. 5 is a flowchart illustrating operations of the production controlsystem 214 in performing a method for controlling and optimizinghydrocarbon production, according to one or more example embodiments.Steps of the method 500 may be performed using the components describedabove with respect to FIG. 2 . One or more blocks in FIG. 5 may beperformed by a computing system such as that shown and described belowin FIGS. 6A and 6B. While the various blocks in FIG. 5 are presented anddescribed sequentially, one of ordinary skill in the art will appreciatethat some or all of the blocks may be executed in different orders, maybe combined or omitted, and some or all of the blocks may be executed inparallel. Furthermore, the blocks may be performed actively orpassively.

At Step 502, the access module 220 accesses sensor data 204 captured byone or more sensors 216 from the well at the first time, and productiondata 206 for the well at the first time. The sensor data and theproduction data may be accessed from a database or may be received fromsensors in real-time.

At Step 504, the one or more processors 222 extract dynamically-relevantprocess data from the sensor data 204 and the production data 206obtained at the first time, using a dynamic mode decomposition (DMD)algorithm. In some example embodiments, the dynamically-relevant processdata characterizes a temporal change in a system state. In someinstances, the dynamically-relevant information is extracted from thecollected data.

At Step 506, the one or more processors 222 train a data-driven model208 for describing production fluid dynamics of the well, based on theextracted dynamically-relevant process data, to predict, for aparameter, an estimated parameter value associated with a second timeand a parameter. The parameter pertains to hydrocarbon production fromthe well. In some example embodiments, the data-driven model 208 is alower-order, non-linear data-driven model. In various exampleembodiments, the parameter is at least one of a multiphase (e.g., oil,gas, or water) production flow rate, a wellbore pressure, or atemperature.

At Step 508, the access module 220 accesses the estimated parametervalue associated with the second time. The parameter value may beaccessed from a database (e.g., the data repository 202).

At Step 510, the one or more hardware processors 222 update thedata-driven model 208 using a data assimilation algorithm and theproduction data 206 received during a production process at the secondtime. In some example embodiments, the updating of the data-driven model208 using the data assimilation algorithm and the production data 206captured during a production process includes adjusting the estimatedparameter value to match an actual measurement value obtained during theproduction process.

At Step 512, the one or more hardware processors 222 generate, using theupdated data-driven model 208, an optimal control setting of a controltool 212 for causing an adjustment to a production system. In someexample embodiments, the control tool 212 is a production valve. Incertain example embodiments, the control tool 212 is a choke assembly.In various example embodiments, the optimal control setting is generatedusing an optimization algorithm to maximize hydrocarbon recovery over aparticular period of production.

At Step 514, the one or more hardware processors 222 generate aninstruction for the control tool 212 to make the adjustment to theproduction system based on the optimal control setting.

At Step 516, the one or more hardware processors 222 execute theinstruction during the production process. The executing of theinstruction causes the adjustment, by the control tool 212, to theproduction system.

Example embodiments may be implemented on a computing system. Anycombination of mobile, desktop, server, router, switch, embedded device,or other types of hardware may be used. For example, as shown in FIG.6A, the computing system 600 may include one or more computer processors602, non-persistent storage 604 (e.g., volatile memory, such as randomaccess memory (RAM) or cache memory), persistent storage 606 (e.g., ahard disk, an optical drive such as a compact disk (CD) drive or digitalversatile disk (DVD) drive, or a flash memory), a communicationinterface 612 (e.g., Bluetooth interface, infrared interface, networkinterface, or optical interface), and numerous other elements andfunctionalities.

The computer processor(s) 602 may be an integrated circuit forprocessing instructions. For example, the computer processor(s) 602 maybe one or more cores or micro-cores of a processor. The computing system600 may also include one or more input devices 610, such as atouchscreen, keyboard, mouse, microphone, touchpad, or electronic pen.

The communication interface 612 may include an integrated circuit forconnecting the computing system 600 to a network (not shown) (e.g., alocal area network (LAN), a wide area network (WAN), such as theInternet, mobile network, or any other type of network) or to anotherdevice, such as another computing device.

Further, the computing system 600 may include one or more output devices608, such as a screen (e.g., a liquid crystal display (LCD), a plasmadisplay, touchscreen, cathode ray tube (CRT) monitor, or projector), aprinter, external storage, or any other output device. One or more ofthe output devices may be the same or different from the inputdevice(s). The input and output device(s) may be locally or remotelyconnected to the computer processor(s) 602, non-persistent storage 604,and persistent storage 606. Many different types of computing systemsexist, and the aforementioned input and output device(s) may take otherforms.

Software instructions in the form of computer readable program code toperform embodiments of the disclosure may be stored, in whole or inpart, temporarily or permanently, on a non-transitory computer readablemedium such as a CD, DVD, storage device, a diskette, a tape, flashmemory, physical memory, or any other computer readable storage medium.Specifically, the software instructions may correspond to computerreadable program code that when executed by a processor(s) is configuredto perform one or more embodiments of the disclosure.

The computing system 600 in FIG. 6A may be connected to or be a part ofa network. For example, as shown in FIG. 6B, the network 616 may includemultiple nodes (e.g., node X 618 or node Y 620). Each node maycorrespond to a computing system, such as the computing system shown inFIG. 6B, or a group of nodes combined may correspond to the computingsystem shown in FIG. 6B. By way of an example, embodiments of thedisclosure may be implemented on a node of a distributed system that isconnected to other nodes. By way of another example, embodiments of thedisclosure may be implemented on a distributed computing system havingmultiple nodes, where each portion of the disclosure may be located on adifferent node within the distributed computing system. Further, one ormore elements of the aforementioned computing system 614 may be locatedat a remote location and connected to the other elements over a network.

Although not shown in FIG. 6B, the node may correspond to a blade in aserver chassis that is connected to other nodes via a backplane. By wayof another example, the node may correspond to a server in a datacenter. By way of another example, the node may correspond to a computerprocessor or micro-core of a computer processor with shared memory orresources.

The nodes (e.g., node X 618 or node Y 620) in the network 616 may beconfigured to provide services for a client device 622. For example, thenodes may be part of a cloud computing system. The nodes may includefunctionality to receive requests from the client device 622 andtransmit responses to the client device 622. The client device 622 maybe a computing system, such as the computing system shown in FIG. 6B.Further, the client device 622 may include or perform all or a portionof one or more embodiments of the disclosure.

The previous description of functions presents only a few examples offunctions performed by the computing system of FIG. 6A and the nodes orclient device in FIG. 6B. Other functions may be performed using one ormore embodiments of the disclosure.

While the disclosure has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the disclosure as disclosed.Accordingly, the scope of the disclosure should be limited only by theattached claims.

Although only a few example embodiments have been described in detailabove, those skilled in the art will readily appreciate that manymodifications are possible in the example embodiments without materiallydeparting from this invention. Accordingly, all such modifications areintended to be included within the scope of this disclosure as definedin the following claims. In the claims, means-plus-function clauses areintended to cover the structures described herein as performing therecited function and not only structural equivalents, but alsoequivalent structures. Thus, although a nail and a screw may not bestructural equivalents in that a nail employs a cylindrical surface tosecure wooden parts together, whereas a screw employs a helical surface,in the environment of fastening wooden parts, a nail and a screw may beequivalent structures. It is the express intention of the applicant notto invoke 35 U.S.C. § 112, paragraph 6 for any limitations of any of theclaims herein, except for those in which the claim expressly uses thewords ‘means for’ together with an associated function.

What is claimed:
 1. A method comprising: accessing an estimatedparameter value associated with a second time and a parameter, theparameter pertaining to production from a well, the estimated parametervalue being predicted by a data-driven model for describing productionfluid dynamics of the well, based on sensor data and production dataobtained at a first time; updating the data-driven model using a dataassimilation algorithm and the production data obtained during aproduction process at the second time, the updating being performedusing one or more hardware processors; and generating, using the updateddata-driven model, an optimal control setting of a control tool forcausing an adjustment to a production system.
 2. The method of claim 1,further comprising: accessing the sensor data captured by one or moresensors from the well at the first time, and production data for thewell at the first time; extracting dynamically relevant process datafrom the sensor data and the production data using a dynamic modedecomposition (DMD) algorithm; and training the data-driven model, basedon the extracted dynamically relevant process data, to predict theestimated parameter value associated with the second time.
 3. The methodof claim 2, wherein the dynamically relevant process data characterizesa temporal change in a system state.
 4. The method of claim 1, furthercomprising: generating an instruction for the control tool to make theadjustment to the production system based on the optimal controlsetting.
 5. The method of claim 4, further comprising: executing theinstruction during the production process, the executing of theinstruction causing the adjustment, by the control tool, to theproduction system.
 6. The method of claim 1, wherein the control tool isa production valve.
 7. The method of claim 1, wherein the control toolis a choke assembly.
 8. The method of claim 1, wherein the data-drivenmodel is a lower-order, non-linear data-driven model.
 9. The method ofclaim 1, wherein the optimal control setting is generated using anoptimization algorithm to maximize hydrocarbon recovery over aparticular period of production.
 10. The method of claim 1, wherein theparameter is at least one of a multiphase production flow rate, awellbore pressure, or a temperature.
 11. The method of claim 1, whereinthe updating of the data-driven model using the data assimilationalgorithm and production data obtained during the production processincludes: adjusting the estimated parameter value to match an actualmeasurement value obtained during the production process.
 12. A systemcomprising: one or more sensors arranged to capture sensor datapertaining to one or more wellhead pressure values in a well; amultiphase flow meter arranged to capture production data pertaining tomultiphase production flow rates of the well; an access moduleconfigured to access an estimated parameter value associated with asecond time and a parameter, the parameter pertaining to production fromthe well, the estimated parameter value being predicted by a data-drivenmodel for describing production fluid dynamics of the well, based on thesensor data and the production data obtained at a first time; and one ormore hardware processors configured to: update the data-driven modelusing a data assimilation algorithm and the production data receivedduring a production process at the second time; and generating, usingthe updated data-driven model, an optimal control setting of a controltool for causing an adjustment to a production system.
 13. The system ofclaim 12, further comprising: an access module configured to access thesensor data captured by one or more sensors from the well at the firsttime, and production data for the well at the first time; wherein theone or more hardware processors are further configured to: extractdynamically relevant process data from the sensor data and theproduction data using a dynamic mode decomposition (DMD) algorithm; andtrain the data-driven model, based on the extracted dynamically relevantprocess data, to predict the estimated parameter value associated withthe second time.
 14. The system of claim 12, wherein the one or morehardware processors are further configured to generate an instructionfor the control tool to make the adjustment to the production systembased on the optimal control setting.
 15. The system of claim 14,wherein the one or more hardware processors are further configured toexecute the instruction during the production process, the executing ofthe instruction causing the adjustment, by the control tool, to theproduction system.
 16. The system of claim 12, wherein the optimalcontrol setting is generated using an optimization algorithm to maximizehydrocarbon recovery over a particular period of production.
 17. Thesystem of claim 12, wherein the parameter is at least one of amultiphase production flow rate, a wellbore pressure, or a temperature.18. The system of claim 12, wherein the updating of the data-drivenmodel using the data assimilation algorithm and production data obtainedduring the production process includes: adjusting the estimatedparameter value to match an actual measurement value obtained during theproduction process.
 19. A non-transitory machine-readable storage mediumcomprising instructions that, when executed by one or more processors ofa machine, cause the machine to perform operations comprising: accessingan estimated parameter value associated with a second time and aparameter, the parameter pertaining to production from a well, theestimated parameter value being predicted by a data-driven model fordescribing production fluid dynamics of the well, based on sensor dataand production data obtained at a first time; updating the data-drivenmodel using a data assimilation algorithm and the production dataobtained during a production process at the second time; and generating,using the updated data-driven model, an optimal control setting of acontrol tool for causing an adjustment to a production system.
 20. Thenon-transitory machine-readable storage medium of claim 19, wherein theoperations further comprise: accessing the sensor data captured by oneor more sensors from the well at the first time, and production data forthe well at the first time; extracting dynamically relevant process datafrom the sensor data and the production data using a dynamic modedecomposition (DMD) algorithm; and training the data-driven model, basedon the extracted dynamically relevant process data, to predict theestimated parameter value associated with the second time.